Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 281-287· 0 citations· 16 references
Abstract
Pneumonia is a respiratory condition, which is critical and needs an accurate diagnosis to minimize death. The following paper introduces a deep learning model, which can be explained, to identify pneumonia in the X-ray images of the chest, using the Convolutional Neural Networks (CNN) architecture. The image resizing, normalization and data augmentation are some of the preprocessing strategies included in the proposed approach to improve the quality of data and the generalization of the model. CNN model is created to automatically identify discriminative features and binomial classification between normal and pneumonia cases with the help of a binary cross-entropy loss and a sigmoid activation function. Gradient-weighted Class Activation Mapping (Grad-CAM) is added to enhance the interpretability of the model by producing visual explanations by highlighting the areas that do affect the model predictions. The results of the experiment prove that the proposed model can show high classification rates (up to 97 percent) and can compete with those of the pretrained models like VGG16, ResNet50, and DenseNet121, at the same time, having lower computational complexity. The suggested framework offers high precision, as well as interpretability, and is an effective instrument to use in the process of computer-aided diagnosis and assist in clinical decision-making in the field of medical imaging.
The proposed deep learning models provide an efficient and accurate tool for multiclass pneumonia detection from CXR images and have the potential to support healthcare professionals in making more accurate diagnoses.
Timothy Karani, Stephen Waithaka· Journal of the Kenya Nationa...· 0 citations
The Pneumonia is a serious respiratory infection that can lead to severe health complications and increased mortality
if not diagnosed and treated at an early stage. Conventional diagnosis using chest X-ray imaging is time-consuming and highly
dependent on the expertise of radiologists, which may not always be readily available in healthcare facilities. To address this
challenge, this project presents an Advanced Pneumonia Detection and Severity Analysis System from Chest X-Ray Images
Using CLAHE-Enhanced Convolutional Neural Networks (CNN) with Grad-CAM Visualization and Clinical Recommendation
Support. The proposed system utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) as a preprocessing
technique to enhance the quality and contrast of chest X-ray images, thereby improving feature extraction and classification
performance. A deep CNN model is trained on labeled chest radiographs to automatically classify images as Pneumonia or
Normal while also assessing the severity level of infection. The model learns discriminative patterns from enhanced X-ray
images and provides accurate predictions with improved robustness. To improve transparency and interpretability, Gradientweighted Class Activation Mapping (Grad-CAM) is integrated into the framework to generate heatmap visualizations that
highlight the infected lung regions responsible for the model’s predictions. The performance of the proposed model is evaluated
using metricssuch as accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results demonstrate that
the integration of CLAHE enhancement and Grad-CAM interpretability significantly improves diagnostic reliability and model
understanding. The developed system offers an efficient, accurate, and explainable AI-assisted solution for early pneumonia
diagnosis, severity assessment, timely treatment support, and improvement
P. M, R. R, M. G. Kousar· International Journal for Re...· 0 citations
Pneumonia screening from chest X-ray (CXR) images is clinically important but often suffers from limited data and class imbalance in public benchmarks. This work develops a transfer-learning-based binary classifier using ResNet50 pretrained on ImageNet. Images are resized to 224×224 and normalized to [0,1]. Online augmentation (rotation, zoom, and shifts) is applied during training. To mitigate imbalance, class weights computed from the training labels are used in the loss. We fine-tune the network by freezing the early layers and updating higher-level layers with a small learning rate (Adam, 1e- 4) and early stopping. On the held-out test set (624 images), the model achieves about 0.88 accuracy. For the pneumonia class, precision, recall, and F1-score reach approximately 0.93, 0.88, and 0.90, respectively. The results indicate that combining class-weighted training with fine-tuned ResNet50 provides a practical baseline for automated pneumonia screening on CXR images.
Danning Dong· International Conference on...· 0 citations
Background: Deep learning models, particularly convolutional neural networks (CNNs), have shown promising performance for pneumonia detection using chest X-ray images. However, the impact of preprocessing, architecture selection, data augmentation, and ensemble strategies has not been systematically evaluated. This study investigated how these factors affect model robustness and diagnostic performance. Methods: A public pediatric chest X-ray dataset was used to systematically evaluate pixel normalization methods, six CNN architectures, progressive data augmentation strategies for class imbalance, and both feature-level and decision-level ensemble approaches. Model performance was assessed by considering not only overall classification accuracy but also clinically relevant risk metrics, particularly false-negative rates. Results: Pixel normalization to the 0–1 range improved model convergence, while Xception and InceptionV3 achieved the best overall performance. Model-specific augmentation strategies were more effective than a fixed 1:1 class ratio for reducing false negatives. Feature-level ensembles tended to overfit, whereas decision-level ensembles provided more stable but only modest performance improvements. Conclusions: These findings demonstrate that reliable medical AI systems require systematic optimization of preprocessing techniques, model architecture, data augmentation strategies, and clinically meaningful evaluation metrics rather than maximizing a single performance indicator. The proposed framework provides practical guidelines for developing robust deep learning models for pneumonia diagnosis in clinical settings.
YongJun Kim, Ji-Yeoun Lee· BioMedInformatics· 0 citations
Due to its late identification and challenging diagnosis, lung cancer continues to be one of the top causes of death for cancer patients globally, positioning it as one of the most critical concerns. Timely identification of cancerous nodules is essential for enhancing the patient’s survival likelihood CT image analysis by hand is not very productive and significantly relies on a specialist’s expertise. In this study, we offer an autonomous lung cancer classification method based on explainable deep learning. The popular DenseNet121 network serves as the foundation for our deep learning model, which is enhanced by the Convolutional Block Attention Module (CBAM). To improve feature extraction of significant spatial and channel properties of input data, attention techniques are added. Furthermore, our method is interpretable because the Grad-CAM technique makes it possible to explain the choices made by a machine learning system. A database of CT scans, comprising 4,598 pictures categorized by large cell carcinoma, adenocarcinoma, and healthy lungs, was utilized. Our evaluations show the model’s effectiveness with an accuracy rate of 94.6\%.
S. Jegadeesan, S. Matheswaran, R. Palanivelrajan· International Conference on...· 0 citations
Pneumonia remains one of the leading causes of morbidity and mortality worldwide, particularly among children, older adults, and immunocompromised individuals. Although chest X-ray (CXR) imaging is widely used for pneumonia diagnosis, manual interpretation is time-consuming, subjective, and highly dependent on radiologist expertise. Deep learning has shown promising performance for automated pneumonia classification; however, class imbalance remains a major challenge that can lead to biased predictions and reduced model generalization. Therefore, this study investigates the effectiveness of image augmentation and class weighting for handling class imbalance in pneumonia classification using chest X-ray images. The main contribution of this study is a systematic comparison of four experimental scenarios: Baseline, Augmentation Only, Class Weighting Only, and Hybrid (Image Augmentation and Class Weighting) implemented on a ResNet50 architecture integrated with the Convolutional Block Attention Module (CBAM). Experiments were conducted using the publicly available Chest X-Ray Images (Pneumonia) dataset from Kaggle, comprising 1,583 Normal and 4,273 Pneumonia images. Model performance was evaluated using Accuracy, ROC-AUC, Precision, Recall, F1-Score, confusion matrix analysis, and Youden’s J-Statistic for threshold optimization. The Hybrid model achieved the best overall performance, with an Accuracy of 95.74%, a ROC-AUC of 98.83%, a Macro Precision of 96.01%, a Macro Recall of 93.13%, and a Macro F1-Score of 94.44%. Moreover, the number of false negative predictions decreased from 25 in the Baseline model to 5 in the Hybrid model. These findings demonstrate that integrating image augmentation and class weighting within the ResNet50-CBAM framework effectively mitigates class imbalance and improves the reliability of automated pneumonia classification.
Kafilah Akhmad Fatahillah, T. H. Saragih, D. Kartini et al.· Indonesian Journal of Electr...· 0 citations
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